{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from H2ighwayNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = HighwayNet(drop = 0.2).to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.1406 Top-1 准确率: 0.0834 Top-5 准确率: 0.2507\n",
      "val 损失: 3.9161 Top-1 准确率: 0.1062 Top-5 准确率: 0.3035\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.5932 Top-1 准确率: 0.1738 Top-5 准确率: 0.4092\n",
      "val 损失: 3.3830 Top-1 准确率: 0.1854 Top-5 准确率: 0.4491\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2253 Top-1 准确率: 0.2443 Top-5 准确率: 0.5077\n",
      "val 损失: 3.1527 Top-1 准确率: 0.2273 Top-5 准确率: 0.5129\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 2.9882 Top-1 准确率: 0.2971 Top-5 准确率: 0.5602\n",
      "val 损失: 2.7669 Top-1 准确率: 0.2942 Top-5 准确率: 0.6110\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.8247 Top-1 准确率: 0.3313 Top-5 准确率: 0.5964\n",
      "val 损失: 2.3998 Top-1 准确率: 0.3757 Top-5 准确率: 0.6889\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6729 Top-1 准确率: 0.3642 Top-5 准确率: 0.6282\n",
      "val 损失: 2.3180 Top-1 准确率: 0.3802 Top-5 准确率: 0.7012\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 2.5650 Top-1 准确率: 0.3883 Top-5 准确率: 0.6505\n",
      "val 损失: 2.7102 Top-1 准确率: 0.3312 Top-5 准确率: 0.6373\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4676 Top-1 准确率: 0.4096 Top-5 准确率: 0.6706\n",
      "val 损失: 2.0757 Top-1 准确率: 0.4508 Top-5 准确率: 0.7557\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3908 Top-1 准确率: 0.4265 Top-5 准确率: 0.6894\n",
      "val 损失: 2.2839 Top-1 准确率: 0.4051 Top-5 准确率: 0.7123\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3071 Top-1 准确率: 0.4436 Top-5 准确率: 0.7039\n",
      "val 损失: 2.3374 Top-1 准确率: 0.3936 Top-5 准确率: 0.7049\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2460 Top-1 准确率: 0.4593 Top-5 准确率: 0.7186\n",
      "val 损失: 1.9395 Top-1 准确率: 0.4811 Top-5 准确率: 0.7768\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1795 Top-1 准确率: 0.4761 Top-5 准确率: 0.7334\n",
      "val 损失: 2.1257 Top-1 准确率: 0.4354 Top-5 准确率: 0.7420\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1336 Top-1 准确率: 0.4856 Top-5 准确率: 0.7416\n",
      "val 损失: 1.9935 Top-1 准确率: 0.4653 Top-5 准确率: 0.7782\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0691 Top-1 准确率: 0.5016 Top-5 准确率: 0.7555\n",
      "val 损失: 1.6186 Top-1 准确率: 0.5557 Top-5 准确率: 0.8411\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0247 Top-1 准确率: 0.5085 Top-5 准确率: 0.7654\n",
      "val 损失: 1.6273 Top-1 准确率: 0.5465 Top-5 准确率: 0.8352\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9772 Top-1 准确率: 0.5196 Top-5 准确率: 0.7738\n",
      "val 损失: 1.7071 Top-1 准确率: 0.5344 Top-5 准确率: 0.8199\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9574 Top-1 准确率: 0.5271 Top-5 准确率: 0.7810\n",
      "val 损失: 1.6574 Top-1 准确率: 0.5470 Top-5 准确率: 0.8302\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9215 Top-1 准确率: 0.5341 Top-5 准确率: 0.7899\n",
      "val 损失: 1.5873 Top-1 准确率: 0.5690 Top-5 准确率: 0.8390\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8670 Top-1 准确率: 0.5464 Top-5 准确率: 0.7987\n",
      "val 损失: 1.6937 Top-1 准确率: 0.5393 Top-5 准确率: 0.8235\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8467 Top-1 准确率: 0.5492 Top-5 准确率: 0.8056\n",
      "val 损失: 1.7132 Top-1 准确率: 0.5430 Top-5 准确率: 0.8160\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8013 Top-1 准确率: 0.5610 Top-5 准确率: 0.8123\n",
      "val 损失: 1.6272 Top-1 准确率: 0.5675 Top-5 准确率: 0.8333\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7713 Top-1 准确率: 0.5660 Top-5 准确率: 0.8196\n",
      "val 损失: 1.6469 Top-1 准确率: 0.5589 Top-5 准确率: 0.8289\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7501 Top-1 准确率: 0.5728 Top-5 准确率: 0.8243\n",
      "val 损失: 1.4466 Top-1 准确率: 0.6030 Top-5 准确率: 0.8581\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7156 Top-1 准确率: 0.5791 Top-5 准确率: 0.8339\n",
      "val 损失: 1.4859 Top-1 准确率: 0.5997 Top-5 准确率: 0.8489\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6917 Top-1 准确率: 0.5828 Top-5 准确率: 0.8380\n",
      "val 损失: 1.4845 Top-1 准确率: 0.5944 Top-5 准确率: 0.8529\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6768 Top-1 准确率: 0.5890 Top-5 准确率: 0.8433\n",
      "val 损失: 1.3689 Top-1 准确率: 0.6278 Top-5 准确率: 0.8701\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6473 Top-1 准确率: 0.5976 Top-5 准确率: 0.8474\n",
      "val 损失: 1.4123 Top-1 准确率: 0.6119 Top-5 准确率: 0.8680\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6300 Top-1 准确率: 0.5991 Top-5 准确率: 0.8508\n",
      "val 损失: 1.3977 Top-1 准确率: 0.6165 Top-5 准确率: 0.8699\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5921 Top-1 准确率: 0.6088 Top-5 准确率: 0.8586\n",
      "val 损失: 1.4802 Top-1 准确率: 0.6010 Top-5 准确率: 0.8534\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5734 Top-1 准确率: 0.6118 Top-5 准确率: 0.8618\n",
      "val 损失: 1.3755 Top-1 准确率: 0.6234 Top-5 准确率: 0.8718\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5623 Top-1 准确率: 0.6146 Top-5 准确率: 0.8642\n",
      "val 损失: 1.3637 Top-1 准确率: 0.6260 Top-5 准确率: 0.8781\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5375 Top-1 准确率: 0.6225 Top-5 准确率: 0.8685\n",
      "val 损失: 1.3619 Top-1 准确率: 0.6308 Top-5 准确率: 0.8775\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5016 Top-1 准确率: 0.6278 Top-5 准确率: 0.8763\n",
      "val 损失: 1.3557 Top-1 准确率: 0.6297 Top-5 准确率: 0.8743\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4901 Top-1 准确率: 0.6318 Top-5 准确率: 0.8798\n",
      "val 损失: 1.2964 Top-1 准确率: 0.6448 Top-5 准确率: 0.8835\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4710 Top-1 准确率: 0.6366 Top-5 准确率: 0.8844\n",
      "val 损失: 1.3320 Top-1 准确率: 0.6347 Top-5 准确率: 0.8811\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4511 Top-1 准确率: 0.6405 Top-5 准确率: 0.8867\n",
      "val 损失: 1.3773 Top-1 准确率: 0.6286 Top-5 准确率: 0.8766\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4297 Top-1 准确率: 0.6464 Top-5 准确率: 0.8916\n",
      "val 损失: 1.3104 Top-1 准确率: 0.6446 Top-5 准确率: 0.8800\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4192 Top-1 准确率: 0.6476 Top-5 准确率: 0.8935\n",
      "val 损失: 1.2985 Top-1 准确率: 0.6445 Top-5 准确率: 0.8838\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4062 Top-1 准确率: 0.6507 Top-5 准确率: 0.8977\n",
      "val 损失: 1.2334 Top-1 准确率: 0.6597 Top-5 准确率: 0.8986\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3902 Top-1 准确率: 0.6538 Top-5 准确率: 0.8994\n",
      "val 损失: 1.3772 Top-1 准确率: 0.6359 Top-5 准确率: 0.8726\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3594 Top-1 准确率: 0.6626 Top-5 准确率: 0.9036\n",
      "val 损失: 1.2825 Top-1 准确率: 0.6539 Top-5 准确率: 0.8879\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3559 Top-1 准确率: 0.6631 Top-5 准确率: 0.9069\n",
      "val 损失: 1.2434 Top-1 准确率: 0.6634 Top-5 准确率: 0.8877\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3315 Top-1 准确率: 0.6673 Top-5 准确率: 0.9107\n",
      "val 损失: 1.3899 Top-1 准确率: 0.6370 Top-5 准确率: 0.8747\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3119 Top-1 准确率: 0.6724 Top-5 准确率: 0.9145\n",
      "val 损失: 1.2446 Top-1 准确率: 0.6673 Top-5 准确率: 0.8877\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3012 Top-1 准确率: 0.6750 Top-5 准确率: 0.9167\n",
      "val 损失: 1.3681 Top-1 准确率: 0.6415 Top-5 准确率: 0.8762\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2783 Top-1 准确率: 0.6790 Top-5 准确率: 0.9213\n",
      "val 损失: 1.1959 Top-1 准确率: 0.6756 Top-5 准确率: 0.9001\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2588 Top-1 准确率: 0.6844 Top-5 准确率: 0.9247\n",
      "val 损失: 1.3085 Top-1 准确率: 0.6550 Top-5 准确率: 0.8871\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2480 Top-1 准确率: 0.6873 Top-5 准确率: 0.9270\n",
      "val 损失: 1.2526 Top-1 准确率: 0.6647 Top-5 准确率: 0.8899\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2579 Top-1 准确率: 0.6845 Top-5 准确率: 0.9297\n",
      "val 损失: 1.4148 Top-1 准确率: 0.6367 Top-5 准确率: 0.8689\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2307 Top-1 准确率: 0.6908 Top-5 准确率: 0.9316\n",
      "val 损失: 1.2517 Top-1 准确率: 0.6680 Top-5 准确率: 0.8915\n",
      "\n",
      "训练完成，耗时 81m 51s\n",
      "最佳验证集准确率: 0.675600\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 1.1046 Top-1 准确率: 0.7234 Top-5 准确率: 0.9360\n",
      "val 损失: 0.9637 Top-1 准确率: 0.7354 Top-5 准确率: 0.9278\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0481 Top-1 准确率: 0.7364 Top-5 准确率: 0.9501\n",
      "val 损失: 0.9471 Top-1 准确率: 0.7436 Top-5 准确率: 0.9283\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0177 Top-1 准确率: 0.7435 Top-5 准确率: 0.9549\n",
      "val 损失: 0.9561 Top-1 准确率: 0.7406 Top-5 准确率: 0.9275\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9965 Top-1 准确率: 0.7488 Top-5 准确率: 0.9589\n",
      "val 损失: 0.9413 Top-1 准确率: 0.7415 Top-5 准确率: 0.9279\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9928 Top-1 准确率: 0.7490 Top-5 准确率: 0.9622\n",
      "val 损失: 0.9428 Top-1 准确率: 0.7441 Top-5 准确率: 0.9299\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9672 Top-1 准确率: 0.7546 Top-5 准确率: 0.9645\n",
      "val 损失: 0.9542 Top-1 准确率: 0.7405 Top-5 准确率: 0.9266\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9737 Top-1 准确率: 0.7510 Top-5 准确率: 0.9674\n",
      "val 损失: 0.9346 Top-1 准确率: 0.7463 Top-5 准确率: 0.9291\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9518 Top-1 准确率: 0.7583 Top-5 准确率: 0.9682\n",
      "val 损失: 0.9445 Top-1 准确率: 0.7432 Top-5 准确率: 0.9281\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9416 Top-1 准确率: 0.7596 Top-5 准确率: 0.9698\n",
      "val 损失: 0.9326 Top-1 准确率: 0.7484 Top-5 准确率: 0.9297\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9392 Top-1 准确率: 0.7605 Top-5 准确率: 0.9708\n",
      "val 损失: 0.9287 Top-1 准确率: 0.7493 Top-5 准确率: 0.9290\n",
      "\n",
      "训练完成，耗时 11m 39s\n",
      "最佳验证集准确率: 0.749300\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9432 Top-1 准确率: 0.7580 Top-5 准确率: 0.9734\n",
      "val 损失: 0.9282 Top-1 准确率: 0.7507 Top-5 准确率: 0.9327\n",
      "\n",
      "第 1/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9256 Top-1 准确率: 0.7630 Top-5 准确率: 0.9731\n",
      "val 损失: 0.9465 Top-1 准确率: 0.7454 Top-5 准确率: 0.9286\n",
      "\n",
      "第 2/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9236 Top-1 准确率: 0.7626 Top-5 准确率: 0.9749\n",
      "val 损失: 0.9386 Top-1 准确率: 0.7445 Top-5 准确率: 0.9294\n",
      "\n",
      "第 3/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9131 Top-1 准确率: 0.7655 Top-5 准确率: 0.9748\n",
      "val 损失: 0.9345 Top-1 准确率: 0.7464 Top-5 准确率: 0.9282\n",
      "\n",
      "第 4/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9139 Top-1 准确率: 0.7635 Top-5 准确率: 0.9759\n",
      "val 损失: 0.9378 Top-1 准确率: 0.7462 Top-5 准确率: 0.9294\n",
      "\n",
      "第 5/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9016 Top-1 准确率: 0.7683 Top-5 准确率: 0.9759\n",
      "val 损失: 0.9278 Top-1 准确率: 0.7497 Top-5 准确率: 0.9307\n",
      "\n",
      "第 6/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9075 Top-1 准确率: 0.7652 Top-5 准确率: 0.9771\n",
      "val 损失: 0.9416 Top-1 准确率: 0.7480 Top-5 准确率: 0.9275\n",
      "\n",
      "第 7/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8920 Top-1 准确率: 0.7691 Top-5 准确率: 0.9788\n",
      "val 损失: 0.9465 Top-1 准确率: 0.7462 Top-5 准确率: 0.9269\n",
      "\n",
      "第 8/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8902 Top-1 准确率: 0.7692 Top-5 准确率: 0.9789\n",
      "val 损失: 0.9458 Top-1 准确率: 0.7458 Top-5 准确率: 0.9287\n",
      "\n",
      "第 9/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8920 Top-1 准确率: 0.7691 Top-5 准确率: 0.9807\n",
      "val 损失: 0.9361 Top-1 准确率: 0.7490 Top-5 准确率: 0.9289\n",
      "\n",
      "第 10/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8959 Top-1 准确率: 0.7673 Top-5 准确率: 0.9795\n",
      "val 损失: 0.9328 Top-1 准确率: 0.7493 Top-5 准确率: 0.9299\n",
      "\n",
      "第 11/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8814 Top-1 准确率: 0.7705 Top-5 准确率: 0.9822\n",
      "val 损失: 0.9403 Top-1 准确率: 0.7498 Top-5 准确率: 0.9282\n",
      "\n",
      "第 12/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8676 Top-1 准确率: 0.7744 Top-5 准确率: 0.9819\n",
      "val 损失: 0.9404 Top-1 准确率: 0.7485 Top-5 准确率: 0.9281\n",
      "\n",
      "第 13/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8717 Top-1 准确率: 0.7736 Top-5 准确率: 0.9819\n",
      "val 损失: 0.9431 Top-1 准确率: 0.7511 Top-5 准确率: 0.9285\n",
      "\n",
      "第 14/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8747 Top-1 准确率: 0.7722 Top-5 准确率: 0.9817\n",
      "val 损失: 0.9808 Top-1 准确率: 0.7406 Top-5 准确率: 0.9248\n",
      "\n",
      "第 15/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8585 Top-1 准确率: 0.7752 Top-5 准确率: 0.9828\n",
      "val 损失: 0.9515 Top-1 准确率: 0.7489 Top-5 准确率: 0.9276\n",
      "\n",
      "第 16/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8662 Top-1 准确率: 0.7738 Top-5 准确率: 0.9838\n",
      "val 损失: 0.9550 Top-1 准确率: 0.7487 Top-5 准确率: 0.9280\n",
      "\n",
      "第 17/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8602 Top-1 准确率: 0.7741 Top-5 准确率: 0.9834\n",
      "val 损失: 0.9529 Top-1 准确率: 0.7467 Top-5 准确率: 0.9279\n",
      "\n",
      "第 18/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8642 Top-1 准确率: 0.7726 Top-5 准确率: 0.9851\n",
      "val 损失: 0.9632 Top-1 准确率: 0.7437 Top-5 准确率: 0.9273\n",
      "\n",
      "第 19/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8539 Top-1 准确率: 0.7761 Top-5 准确率: 0.9850\n",
      "val 损失: 0.9599 Top-1 准确率: 0.7456 Top-5 准确率: 0.9279\n",
      "\n",
      "第 20/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8609 Top-1 准确率: 0.7737 Top-5 准确率: 0.9855\n",
      "val 损失: 0.9568 Top-1 准确率: 0.7490 Top-5 准确率: 0.9273\n",
      "\n",
      "第 21/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8422 Top-1 准确率: 0.7770 Top-5 准确率: 0.9864\n",
      "val 损失: 0.9799 Top-1 准确率: 0.7434 Top-5 准确率: 0.9248\n",
      "\n",
      "第 22/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8328 Top-1 准确率: 0.7815 Top-5 准确率: 0.9856\n",
      "val 损失: 0.9561 Top-1 准确率: 0.7478 Top-5 准确率: 0.9294\n",
      "\n",
      "第 23/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8379 Top-1 准确率: 0.7789 Top-5 准确率: 0.9867\n",
      "val 损失: 0.9668 Top-1 准确率: 0.7493 Top-5 准确率: 0.9265\n",
      "\n",
      "第 24/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8401 Top-1 准确率: 0.7775 Top-5 准确率: 0.9878\n",
      "val 损失: 0.9573 Top-1 准确率: 0.7504 Top-5 准确率: 0.9309\n",
      "\n",
      "第 25/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8384 Top-1 准确率: 0.7775 Top-5 准确率: 0.9877\n",
      "val 损失: 0.9785 Top-1 准确率: 0.7460 Top-5 准确率: 0.9260\n",
      "\n",
      "第 26/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8336 Top-1 准确率: 0.7792 Top-5 准确率: 0.9880\n",
      "val 损失: 0.9660 Top-1 准确率: 0.7465 Top-5 准确率: 0.9270\n",
      "\n",
      "第 27/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8272 Top-1 准确率: 0.7799 Top-5 准确率: 0.9889\n",
      "val 损失: 0.9645 Top-1 准确率: 0.7475 Top-5 准确率: 0.9288\n",
      "\n",
      "第 28/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8271 Top-1 准确率: 0.7798 Top-5 准确率: 0.9881\n",
      "val 损失: 0.9692 Top-1 准确率: 0.7449 Top-5 准确率: 0.9262\n",
      "\n",
      "第 29/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8150 Top-1 准确率: 0.7840 Top-5 准确率: 0.9887\n",
      "val 损失: 0.9822 Top-1 准确率: 0.7434 Top-5 准确率: 0.9247\n",
      "\n",
      "训练完成，耗时 24m 12s\n",
      "最佳验证集准确率: 0.751100\n"
     ]
    }
   ],
   "source": [
    "model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) = train_model(model2,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 30)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model3, 'fixed90epoch_highway+02drop.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的隐藏层是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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